基于共享评级迁移的跨域推荐算法

数据稀疏和用户冷启动一直是推荐系统中亟待解决的问题,因此提出了一种基于共享评级迁移的跨域推荐算法(shared ratings transfer cross-domain recommendation,SRTCD).首先,该算法考虑到不同领域间存在着用户群体/项目信息潜在特征的相似性,对各个领域评分矩阵进行概率矩阵分解,得到用户和项目的潜在特征;再利用基于模拟退火和遗传算法优化的K-means算法对用户和项目分别进行聚类,将用户类别和项目类别的内积作为共享评级;然后利用各领域数据集的共享评级和目标领域数据集的特定评级得出推荐结果.最后,利用公共数据集对所提方法SRTCD进行验证,结果表明该方法的推荐性能明显优于常用推荐算法.

Cross domain recommendation algorithm based on shared ratings transfer

Data sparsity and user cold start are always the problems to be solved in the recommendation system. This paper proposed a cross domain recommendation algorithm SRTCD. Considering the similarity of latent factors of user groups / items in different domains, the algorithm decomposed the rating matrix of each domain into probability matrix to obtain the latent factors of users and items. Then, it used K-means algorithm based on simulated annealing and genetic algorithm to cluster users and items respectively, and used the inner product of user category and items category as shared rating. Then, it obtained the recommendation results by using the shared rating of each domain dataset and the specific rating of the target domain dataset. Finally, it verified the proposed method by public data sets, and the results show that the performance of SRTCD is significantly better than that of common recommendation algorithms.